# How do you measure multi-label classification accuracy?

Multi-label assignment is the task in machine learning to assign to each input value a set of categories from a fixed vocabulary where the categories need not be statistically independent, so precluding building a set of independent classifiers each classifying the inputs as belong to each of the categories or not.

Machine learning also needs a measure by which the model may be evaluated. So this is the question how do we evaluate a multi-label classifier?

We can’t use the normal recall, accuracy and F measures since they require a binary is it correct or not measure of each categorisation. Without such a measure we have no obvious means to evaluate models nor to measure concept drift.

• I like this discussion of accuracy: stats.stackexchange.com/q/312780/247274. Two example of the strictly proper scoring rules that Kolassa discusses are crossentropy loss (log loss) and Brier score.
– Dave
Oct 28, 2020 at 20:04